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» Sequence Complexity for Biological Sequence Analysis
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BMCBI
2010
105views more  BMCBI 2010»
13 years 9 months ago
Subdivision of the MDR superfamily of medium-chain dehydrogenases/reductases through iterative hidden Markov model refinement
Background: The Medium-chain Dehydrogenases/Reductases (MDR) form a protein superfamily whose size and complexity defeats traditional means of subclassification; it currently has ...
Joel Hedlund, Hans Jörnvall, Bengt Persson
EVOW
2010
Springer
13 years 7 months ago
Finding Gapped Motifs by a Novel Evolutionary Algorithm
Background: Identifying approximately repeated patterns, or motifs, in DNA sequences from a set of co-regulated genes is an important step towards deciphering the complex gene reg...
Chengwei Lei, Jianhua Ruan
BIODATAMINING
2008
178views more  BIODATAMINING 2008»
13 years 9 months ago
Clustering-based approaches to SAGE data mining
Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical appli...
Haiying Wang, Huiru Zheng, Francisco Azuaje
BMCBI
2008
116views more  BMCBI 2008»
13 years 9 months ago
IgTM: An algorithm to predict transmembrane domains and topology in proteins
Background: Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI...
Piedachu Peris, Damián López, Marcel...
BMCBI
2004
208views more  BMCBI 2004»
13 years 9 months ago
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
Vadim Alexandrov, Mark Gerstein